'''

@author: Administrator
'''
import mnist.input_data as input_data
import tensorflow as tf

mn = input_data.read_data_sets("./", one_hot=True)

x = tf.placeholder(tf.float32, [None, 784])
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
y = tf.nn.softmax(tf.matmul(x, W) + b)

y_ = tf.placeholder(tf.float32, [None, 10])

cross_entropy = tf.reduce_sum(y_ * tf.log(y))
train_step  = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)

init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)

for i in range(1000):
    batch_xs, batch_ys = mn.train.next_batch(100)
    sess.run(train_step, feed_dict={x: batch_xs, y_:batch_ys})

correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print(sess.run(accuracy, feed_dict={x:mn.test.images, y_: mn.test.labels}))

#   # Test trained model
# correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
# accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
# print(sess.run(accuracy, feed_dict={x: mn.test.images,
#                                       y_: mn.test.labels}))
sess.close()

